A novel approach for identification of PV cell model parameters with Grey Wolf Election-Based Optimization algorithm

Abstract

In the past few years, photovoltaic production has significantly increased worldwide and has become a necessary element for achieving global agreements to minimize carbon dioxide emissions. Therefore, a precise and reliable simulation of PV panels is of increasing interest to researchers. However, this task is predicated on determining the PV model parameters; thus, it continues to be a highly challenging task. To overcome this challenge, we propose a swarm-based human optimization algorithm, i.e. the Grey Wolf Election-Based Optimization algorithm. This new approach combines inventive human behavior in the election process with the powerful search ability of grey wolves. The root-mean-square deviations predicted by the Grey Wolf Election-Based Optimization algorithm for a RTC France cell are 7.7386E-04, 7.5644E-04, and 7.4965E-04 for single, double, and triple-diode models, respectively. A total of thirteen of the most cited and newest meta-heuristic algorithms were employed as competitors to adequately assess the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm outperformed the compared algorithms and provided more efficient solutions.